Trailer Reimagined: An Innovative, Llm-DRiven, Expressive Automated Movie Summary framework (TRAILDREAMS)

2025-07-28

Online Journal of Communication and Media Technologies

AI Summary

This work proposes an end-to-end framework for automatically generating movie trailers, aiming to reduce reliance on manual creative labor. The approach uniquely integrates large language models (LLMs) deeply into the trailer generation pipeline, enabling intelligent selection of key visual scenes and dialogue while simultaneously synthesizing background music and voiceover narration. By combining video clip analysis, dialogue extraction, and audio-visual synthesis techniques, the system achieves significantly higher audience ratings compared to current state-of-the-art methods. Although its performance remains slightly below that of human-crafted trailers, this study represents a meaningful advance toward automated generation of creative multimedia content.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper introduces TRAILDREAMS, a framework that uses a large language model (LLM) to automate the production of movie trailers. The purpose of LLM is to select key visual sequences and impactful dialogues, and to help TRAILDREAMS to generate audio elements such as music and voiceovers. The goal is to produce engaging and visually appealing trailers efficiently. In comparative evaluations, TRAILDREAMS surpasses current state-of-the-art trailer generation methods in viewer ratings. However, it still falls short when compared to real, human-crafted trailers. While TRAILDREAMS demonstrates significant promise and marks an advancement in automated creative processes, further improvements are necessary to bridge the quality gap with traditional trailers.

Problem

• movie trailer generation
• large language model
• automated video editing
• creative AI
• multimodal content generation

Innovation

• LLM-driven
• automated movie trailer
• expressive summarization
• multimodal generation
• creative AI